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from typing import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...modules import sparse as sp
from ...utils.random_utils import hammersley_sequence
from .base import SparseTransformerBase
from ...representations import Gaussian
class SLatGaussianDecoder(SparseTransformerBase):
def __init__(
self,
resolution: int,
model_channels: int,
latent_channels: int,
num_blocks: int,
num_heads: Optional[int] = None,
num_head_channels: Optional[int] = 64,
mlp_ratio: float = 4,
attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin",
window_size: int = 8,
pe_mode: Literal["ape", "rope"] = "ape",
use_fp16: bool = False,
use_checkpoint: bool = False,
qk_rms_norm: bool = False,
representation_config: dict = None,
):
super().__init__(
in_channels=latent_channels,
model_channels=model_channels,
num_blocks=num_blocks,
num_heads=num_heads,
num_head_channels=num_head_channels,
mlp_ratio=mlp_ratio,
attn_mode=attn_mode,
window_size=window_size,
pe_mode=pe_mode,
use_fp16=use_fp16,
use_checkpoint=use_checkpoint,
qk_rms_norm=qk_rms_norm,
)
self.resolution = resolution
self.rep_config = representation_config
self._calc_layout()
self.out_layer = sp.SparseLinear(model_channels, self.out_channels)
self._build_perturbation()
self.initialize_weights()
if use_fp16:
self.convert_to_fp16()
def initialize_weights(self) -> None:
super().initialize_weights()
# Zero-out output layers:
nn.init.constant_(self.out_layer.weight, 0)
nn.init.constant_(self.out_layer.bias, 0)
def _build_perturbation(self) -> None:
perturbation = [hammersley_sequence(3, i, self.rep_config['num_gaussians']) for i in range(self.rep_config['num_gaussians'])]
perturbation = torch.tensor(perturbation).float() * 2 - 1
perturbation = perturbation / self.rep_config['voxel_size']
perturbation = torch.atanh(perturbation).to(self.device)
self.register_buffer('offset_perturbation', perturbation)
def _calc_layout(self) -> None:
self.layout = {
'_xyz' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3},
'_features_dc' : {'shape': (self.rep_config['num_gaussians'], 1, 3), 'size': self.rep_config['num_gaussians'] * 3},
'_scaling' : {'shape': (self.rep_config['num_gaussians'], 3), 'size': self.rep_config['num_gaussians'] * 3},
'_rotation' : {'shape': (self.rep_config['num_gaussians'], 4), 'size': self.rep_config['num_gaussians'] * 4},
'_opacity' : {'shape': (self.rep_config['num_gaussians'], 1), 'size': self.rep_config['num_gaussians']},
}
start = 0
for k, v in self.layout.items():
v['range'] = (start, start + v['size'])
start += v['size']
self.out_channels = start
def to_representation(self, x: sp.SparseTensor) -> List[Gaussian]:
"""
Convert a batch of network outputs to 3D representations.
Args:
x: The [N x * x C] sparse tensor output by the network.
Returns:
list of representations
"""
ret = []
for i in range(x.shape[0]):
representation = Gaussian(
sh_degree=0,
aabb=[-0.5, -0.5, -0.5, 1.0, 1.0, 1.0],
mininum_kernel_size = self.rep_config['3d_filter_kernel_size'],
scaling_bias = self.rep_config['scaling_bias'],
opacity_bias = self.rep_config['opacity_bias'],
scaling_activation = self.rep_config['scaling_activation']
)
xyz = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution
for k, v in self.layout.items():
if k == '_xyz':
offset = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape'])
offset = offset * self.rep_config['lr'][k]
if self.rep_config['perturb_offset']:
offset = offset + self.offset_perturbation
offset = torch.tanh(offset) / self.resolution * 0.5 * self.rep_config['voxel_size']
_xyz = xyz.unsqueeze(1) + offset
setattr(representation, k, _xyz.flatten(0, 1))
else:
feats = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']).flatten(0, 1)
feats = feats * self.rep_config['lr'][k]
setattr(representation, k, feats)
ret.append(representation)
return ret
def forward(self, x: sp.SparseTensor) -> List[Gaussian]:
h = super().forward(x)
h = h.type(x.dtype)
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
h = self.out_layer(h)
return self.to_representation(h)
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